Field of the Invention
The present invention relates to a quantum mechanical machine vision system and an arithmetic operation method, more specifically to a quantum mechanical machine vision system and an arithmetic operation method based on quantum dot.
Discussion of the Background
Human beings now have better analytical capabilities than machine analysis in many areas such as object recognition, knowledge representation, reasoning, learning and natural language processing. Accordingly, in order to imitate or surpass the human way of thinking mechanically, a complicated arithmetic operation method must be used.
An accurate solution to the problem of optimization of machine vision system is required to imitate or surpass human visual recognition ability as an example.
In order to solve the complex computation method of artificial view, there is a method of performing quantum mechanical calculation using quantum computing.
A quantum computer is a physical system that uses one or more quantum effects to perform calculations. A quantum computer capable of efficiently simulating other quantum computers is called a universal quantum computer (UQC).
1. Approach to Quantum Computation
There are several general approaches to the design and operation of quantum computers.
One approach corresponds to a ‘circuit model’ of quantum computation. In this approach, qubits operate in the order of a logical gate, which is a representation of a compiled algorithm. Circuit model quantum computers have some serious barriers in their actual implementation. In a circuit model, qubits are required to be coherent for a longer period of time than a single-gate time. This demand arises because circuit model quantum computers require operations, called quantum error correction, to operate. Quantum error correction cannot be performed without the qubit of a circuit model quantum computer that can maintain quantum coherence for a time interval of about 1000 times one gate time. There have been a number of studies focused on developing qubits with sufficient coherence to form basic information units of quantum computers. This is described in, for example, “Introduction to Quantum Algorithms”, by Shor, P. W. arXiv. org: quantph/0005003 (2001), pp. 1-27. This technical field is still stagnant due to the lack of the ability to enhance the coherence of the qubit to a level suitable for designing and operating real circuit model quantum computers.
2. Computational Complexity Theory
In computer science, computational complexity theory is a kind of computational theory required to solve a given computational problem and the theory of computation to study resources or costs. Costs are generally measured by abstract parameters called computational resources, such as time and space. The time means the number of steps necessary to solve the problem, and the space means the required amount of information storage or the amount of memory required.
Optimization problems correspond to problems where one or more objective functions are minimized and maximized under a set of constraints, sometimes with respect to a set of variables.
Simulation problems typically deal with the simulation of one system by another system during a typical time interval. For example, computer simulations consist of business processes, ecological habitats, protein folding, molecular ground states, and quantum systems. These problems often involve numerous diverse entities that are different from complex interrelationships and behavioral rules. Feynman suggests that a quantum system can be used to simulate several physical systems more efficiently than UTM.
Many optimization and simulation problems cannot be solved using UTM. Because of these limitations, computational elements are needed that can solve computational problems beyond the scope of the UTM. Other digital computer based systems and methods for solving optimization problems can be found.
An example of a technique for solving this optimization problem is described in Korean Patent No. 10-1309677 entitled ‘Method for Calculating Adoptive Quantum’.
The prior art discloses a quantum computing method using a quantum system that includes a plurality of qubits. In the prior art, quantum annealing is possible to obtain a desired minimum energy (or cost), which concurrently tracks a configuration of a superposition state, and especially, Adiabatic Quantum Computation (AQC) technique is used to perform quantum annealing. In addition, AQC uses a technique in which an adiabatic change of Hamiltonian from the initial state to the target state is obtained and a solution of the desired target state is finally obtained.
The above prior art describes the general operation of a quantum computing system to solve a complex problem, and in spite of the existence of this prior art, the selection of an optimized quantum system remains a very important problem to solve the complicated matter.